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  • Brand : BIOFRON

  • Catalogue Number : AV-P11822

  • Specification : 98%

  • CAS number : 76-77-7

  • Formula : C22H30O6

  • Molecular Weight : 390.47

  • PUBCHEM ID : 283906

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Catalogue Number


Analysis Method






Molecular Weight




Botanical Source

Structure Type










1.2±0.1 g/cm3


Soluble in Chloroform,Dichloromethane,Ethyl Acetate,DMSO,Acetone,etc.

Flash Point

195.5±23.6 °C

Boiling Point

567.5±50.0 °C at 760 mmHg

Melting Point



InChl Key

WGK Germany


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Personal Projective Equipment

Correct Usage

For Reference Standard and R&D, Not for Human Use Directly.

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provides coniferyl ferulate(CAS#:76-77-7) MSDS, density, melting point, boiling point, structure, formula, molecular weight etc. Articles of coniferyl ferulate are included as well.>> amp version: coniferyl ferulate

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Several studies have measured health outcomes in the United States, but none have provided a comprehensive assessment of patterns of health by state.

To use the results of the Global Burden of Disease Study (GBD) to report trends in the burden of diseases, injuries, and risk factors at the state level from 1990 to 2016.

Design and Setting
A systematic analysis of published studies and available data sources estimates the burden of disease by age, sex, geography, and year.

Main Outcomes and Measures
Prevalence, incidence, mortality, life expectancy, healthy life expectancy (HALE), years of life lost (YLLs) due to premature mortality, years lived with disability (YLDs), and disability-adjusted life-years (DALYs) for 333 causes and 84 risk factors with 95% uncertainty intervals (UIs) were computed.

Between 1990 and 2016, overall death rates in the United States declined from 745.2 (95% UI, 740.6 to 749.8) per 100 000 persons to 578.0 (95% UI, 569.4 to 587.1) per 100 000 persons. The probability of death among adults aged 20 to 55 years declined in 31 states and Washington, DC from 1990 to 2016. In 2016, Hawaii had the highest life expectancy at birth (81.3 years) and Mississippi had the lowest (74.7 years), a 6.6-year difference. Minnesota had the highest HALE at birth (70.3 years), and West Virginia had the lowest (63.8 years), a 6.5-year difference. The leading causes of DALYs in the United States for 1990 and 2016 were ischemic heart disease and lung cancer, while the third leading cause in 1990 was low back pain, and the third leading cause in 2016 was chronic obstructive pulmonary disease. Opioid use disorders moved from the 11th leading cause of DALYs in 1990 to the 7th leading cause in 2016, representing a 74.5% (95% UI, 42.8% to 93.9%) change. In 2016, each of the following 6 risks individually accounted for more than 5% of risk-attributable DALYs: tobacco consumption, high body mass index (BMI), poor diet, alcohol and drug use, high fasting plasma glucose, and high blood pressure. Across all US states, the top risk factors in terms of attributable DALYs were due to 1 of the 3 following causes: tobacco consumption (32 states), high BMI (10 states), or alcohol and drug use (8 states).

Conclusions and Relevance
There are wide differences in the burden of disease at the state level. Specific diseases and risk factors, such as drug use disorders, high BMI, poor diet, high fasting plasma glucose level, and alcohol use disorders are increasing and warrant increased attention. These data can be used to inform national health priorities for research, clinical care, and policy.


Question How have the levels and trends of burden of diseases, injuries, and risk factors in the United States changed from 1990 to 2016 by state? Findings This study, involving examination of 333 causes and 84 risk factors, demonstrated that health in the United States improved from 1990 to 2016, although the drivers of mortality and morbidity have changed in some states, with specific risk factors such as drug use disorders, high body mass index (BMI), and alcohol use disorders being associated with adverse outcomes. In 5 states, the probability of death between ages 20 and 55 years has increased more than 10% between 1990 and 2016. Meaning Differences in health outcomes and drivers of morbidity and mortality at the state level indicate the need for greater investment in preventive and medical care across the life course. The intersection of risk, mortality, and morbidity in particular geographic areas needs to be further explored at the state level.


The State of US Health, 1990-2016 Burden of Diseases, Injuries, and Risk Factors Among US States The US Burden of Disease Collaborators


Ali H. Mokdad, PhD,1 Katherine Ballestros, PhD,1 Michelle Echko, BS,1 Scott Glenn, MSc,1 Helen E. Olsen, MA,1 Erin Mullany, BA,1 Alex Lee, BS,1 Abdur Rahman Khan, MD,2 Alireza Ahmadi, MD,3,4 Alize J. Ferrari, PhD,1,5,6 Amir Kasaeian, PhD,7 Andrea Werdecker, PhD,8 Austin Carter, BS,1 Ben Zipkin, BS,1 Benn Sartorius, PhD,9,10 Berrin Serdar, PhD,11 Bryan L. Sykes, PhD,12 Chris Troeger, MPH,1 Christina Fitzmaurice, MD,1,13 Colin D. Rehm, PhD,14 Damian Santomauro, PhD,1,5,6 Daniel Kim, DrPH,15 Danny Colombara, PhD,1 David C. Schwebel, PhD,16 Derrick Tsoi, BS,1 Dhaval Kolte, MD,17 Elaine Nsoesie, PhD,1 Emma Nichols, BA,1 Eyal Oren, PhD,18 Fiona J. Charlson, PhD,1,5,6 George C. Patton, MD,19 Gregory A. Roth, MD,1 H. Dean Hosgood, PhD,20 Harvey A. Whiteford, PhD,1,5,6 Hmwe Kyu, PhD,1 Holly E. Erskine, PhD,1,5,6 Hsiang Huang, MD,21 Ira Martopullo, MPH,1 Jasvinder A. Singh, MD,16 Jean B. Nachega, PhD,22,23,24 Juan R. Sanabria, MD,25,26 Kaja Abbas, PhD,27 Kanyin Ong, PhD,1 Karen Tabb, PhD,28 Kristopher J. Krohn, MPH,1 Leslie Cornaby, BS,1 Louisa Degenhardt, PhD,1,29 Mark Moses, MHS,1 Maryam Farvid, PhD,30,31 Max Griswold, MA,1 Michael Criqui, MD,32 Michelle Bell, PhD,33 Minh Nguyen, BS,1 Mitch Wallin, MD,34,35 Mojde Mirarefin, MPH,1,36 Mostafa Qorbani, PhD,37 Mustafa Younis, DrPH,38 Nancy Fullman, MPH,1 Patrick Liu, MPH,1 Paul Briant, BS,1 Philimon Gona, PhD,39 Rasmus Havmoller, PhD,4 Ricky Leung, PhD,40 Ruth Kimokoti, MD,41 Shahrzad Bazargan-Hejazi, PhD,42,43 Simon I. Hay, DSc,1,44 Simon Yadgir, BS,1 Stan Biryukov, BS,1 Stein Emil Vollset, DrPH,1,45 Tahiya Alam, MPH,1 Tahvi Frank, BS,1 Talha Farid, MD,2 Ted Miller, PhD,46,47 Theo Vos, PhD,1 Till Barnighausen, MD,48,49,50 Tsegaye Telwelde Gebrehiwot, MPH,51 Yuichiro Yano, MD,52 Ziyad Al-Aly, MD,53 Alem Mehari, MD,54 Alexis Handal, PhD,55 Amit Kandel, MBBS,56 Ben Anderson, MD,57 Brian Biroscak, PhD,33,58 Dariush Mozaffarian, MD,59 E. Ray Dorsey, MD,60 Eric L. Ding, ScD,30 Eun-Kee Park, PhD,61 Gregory Wagner, MD,62 Guoqing Hu, PhD,63 Honglei Chen, PhD,64 Jacob E. Sunshine, MD,57 Jagdish Khubchandani, PhD,65 Janet Leasher, OD,66 Janni Leung, PhD,57,67 Joshua Salomon, PhD,48 Jurgen Unutzer, MD,57 Leah Cahill, PhD,30,68 Leslie Cooper, MD,69 Masako Horino, MPH,70 Michael Brauer, ScD,1,71 Nicholas Breitborde, PhD,72 Peter Hotez, PhD,73 Roman Topor-Madry, PhD,74,75 Samir Soneji, PhD,76 Saverio Stranges, PhD,77,78 Spencer James, MD,1 Stephen Amrock, MD,79 Sudha Jayaraman, MD,80 Tejas Patel, MD,81 Tomi Akinyemiju, PhD,16 Vegard Skirbekk, PhD,82,83 Yohannes Kinfu, PhD,84 Zulfiqar Bhutta, PhD,85,86 Jost B. Jonas, MD,87 and Christopher J. L. Murray, DPhilcorresponding author1

Publish date

2018 Apr 10;




Measurement of changes in health across locations is useful to compare and contrast changing epidemiological patterns against health system performance and identify specific needs for resource allocation in research, policy development, and programme decision making. Using the Global Burden of Diseases, Injuries, and Risk Factors Study 2016, we drew from two widely used summary measures to monitor such changes in population health: disability-adjusted life-years (DALYs) and healthy life expectancy (HALE). We used these measures to track trends and benchmark progress compared with expected trends on the basis of the Socio-demographic Index (SDI).

We used results from the Global Burden of Diseases, Injuries, and Risk Factors Study 2016 for all-cause mortality, cause-specific mortality, and non-fatal disease burden to derive HALE and DALYs by sex for 195 countries and territories from 1990 to 2016. We calculated DALYs by summing years of life lost and years of life lived with disability for each location, age group, sex, and year. We estimated HALE using age-specific death rates and years of life lived with disability per capita. We explored how DALYs and HALE differed from expected trends when compared with the SDI: the geometric mean of income per person, educational attainment in the population older than age 15 years, and total fertility rate.

The highest globally observed HALE at birth for both women and men was in Singapore, at 75·2 years (95% uncertainty interval 71·9-78·6) for females and 72·0 years (68·8-75·1) for males. The lowest for females was in the Central African Republic (45·6 years [42·0-49·5]) and for males was in Lesotho (41·5 years [39·0-44·0]). From 1990 to 2016, global HALE increased by an average of 6·24 years (5·97-6·48) for both sexes combined. Global HALE increased by 6·04 years (5·74-6·27) for males and 6·49 years (6·08-6·77) for females, whereas HALE at age 65 years increased by 1·78 years (1·61-1·93) for males and 1·96 years (1·69-2·13) for females. Total global DALYs remained largely unchanged from 1990 to 2016 (-2·3% [-5·9 to 0·9]), with decreases in communicable, maternal, neonatal, and nutritional (CMNN) disease DALYs offset by increased DALYs due to non-communicable diseases (NCDs). The exemplars, calculated as the five lowest ratios of observed to expected age-standardised DALY rates in 2016, were Nicaragua, Costa Rica, the Maldives, Peru, and Israel. The leading three causes of DALYs globally were ischaemic heart disease, cerebrovascular disease, and lower respiratory infections, comprising 16·1% of all DALYs. Total DALYs and age-standardised DALY rates due to most CMNN causes decreased from 1990 to 2016. Conversely, the total DALY burden rose for most NCDs; however, age-standardised DALY rates due to NCDs declined globally.

At a global level, DALYs and HALE continue to show improvements. At the same time, we observe that many populations are facing growing functional health loss. Rising SDI was associated with increases in cumulative years of life lived with disability and decreases in CMNN DALYs offset by increased NCD DALYs. Relative compression of morbidity highlights the importance of continued health interventions, which has changed in most locations in pace with the gross domestic product per person, education, and family planning. The analysis of DALYs and HALE and their relationship to SDI represents a robust framework with which to benchmark location-specific health performance. Country-specific drivers of disease burden, particularly for causes with higher-than-expected DALYs, should inform health policies, health system improvement initiatives, targeted prevention efforts, and development assistance for health, including financial and research investments for all countries, regardless of their level of sociodemographic development. The presence of countries that substantially outperform others suggests the need for increased scrutiny for proven examples of best practices, which can help to extend gains, whereas the presence of underperforming countries suggests the need for devotion of extra attention to health systems that need more robust support.

Bill & Melinda Gates Foundation.


Global, regional, and national disability-adjusted life-years (DALYs) for 333 diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016


GBD 2016 DALYs and HALE Collaborators†

Publish date

2017 Sep 16;




Chronic diseases and conditions (e.g., heart diseases, stroke, arthritis, and diabetes) are the leading causes of morbidity and mortality in the United States. These conditions are costly to the U.S. economy, yet they are often preventable or controllable. Behavioral risk factors (e.g., excessive alcohol consumption, tobacco use, poor diet, frequent mental distress, and insufficient sleep) are linked to the leading causes of morbidity and mortality. Adopting positive health behaviors (e.g., staying physically active, quitting tobacco use, obtaining routine physical checkups, and checking blood pressure and cholesterol levels) can reduce morbidity and mortality from chronic diseases and conditions. Monitoring the health risk behaviors, chronic diseases and conditions, access to health care, and use of preventive health services at multilevel public health points (states, territories, and metropolitan and micropolitan statistical areas [MMSA]) can provide important information for development and evaluation of health intervention programs.

Reporting Period
2013 and 2014.

Description of the System
The Behavioral Risk Factor Surveillance System (BRFSS) is an ongoing, state-based, random-digit-dialed telephone survey of noninstitutionalized adults aged ≥18 years residing in the United States. BRFSS collects data on health risk behaviors, chronic diseases and conditions, access to health care, and use of preventive health services and practices related to the leading causes of death and disability in the United States and participating territories. This is the first BRFSS report to include age-adjusted prevalence estimates. For 2013 and 2014, these age-adjusted prevalence estimates are presented for all 50 states, the District of Columbia, the Commonwealth of Puerto Rico, Guam, and selected MMSA.

Age-adjusted prevalence estimates of health status indicators, health care access and preventive practices, health risk behaviors, chronic diseases and conditions, and cardiovascular conditions vary by state, territory, and MMSA. Each set of proportions presented refers to the range of age-adjusted prevalence estimates of selected BRFSS measures as reported by survey respondents.

The following are estimates for 2013. Adults reporting frequent mental distress: 7.7%-15.2% in states and territories and 6.3%-19.4% in MMSA. Adults with inadequate sleep: 27.6%-49.2% in states and territories and 26.5%-44.4% in MMSA. Adults aged 18-64 years having health care coverage: 66.9%-92.4% in states and territories and 60.5%-97.6% in MMSA. Adults identifying as current cigarette smokers: 10.1%-28.8% in states and territories and 6.1%-33.6% in MMSA. Adults reporting binge drinking during the past month: 10.5%-25.2% in states and territories and 7.2%-25.3% in MMSA. Adults with obesity: 21.0%-35.2% in states and territories and 12.1%-37.1% in MMSA. Adults aged ≥45 years with some form of arthritis: 30.6%-51.0% in states and territories and 27.6%-52.4% in MMSA. Adults aged ≥45 years who have had coronary heart disease: 7.4%-17.5% in states and territories and 6.2%-20.9% in MMSA. Adults aged ≥45 years who have had a stroke: 3.1%-7.5% in states and territories and 2.3%-9.4% in MMSA. Adults with high blood pressure: 25.2%-40.1% in states and territories and 22.2%-42.2% in MMSA. Adults with high blood cholesterol: 28.8%-38.4% in states and territories and 26.3%-39.6% in MMSA.

The following are estimates for 2014. Adults reporting frequent physical distress: 7.8%-16.0% in states and territories and 6.2%-18.5% in MMSA. Women aged 21-65 years who had a Papanicolaou test during the past 3 years: 67.7%-87.8% in states and territories and 68.0%-94.3% in MMSA. Adults aged 50-75 years who received colorectal cancer screening on the basis of the 2008 U.S. Preventive Services Task Force recommendation: 42.8%-76.7% in states and territories and 49.1%-79.6% in MMSA. Adults with inadequate sleep: 28.4%-48.6% in states and territories and 25.4%-45.3% in MMSA. Adults reporting binge drinking during the past month: 10.7%-25.1% in states and territories and 6.7%-26.3% in MMSA. Adults aged ≥45 years who have had coronary heart disease: 8.0%-17.1% in states and territories and 7.6%-19.2% in MMSA. Adults aged ≥45 years with some form of arthritis: 31.2%-54.7% in states and territories and 28.4%-54.7% in MMSA. Adults with obesity: 21.0%-35.9% in states and territories and 19.7%-42.5% in MMSA.

Prevalence of certain chronic diseases and conditions, health risk behaviors, and use of preventive health services varies among states, territories, and MMSA. The findings of this report highlight the need for continued monitoring of health status, health care access, health behaviors, and chronic diseases and conditions at state and local levels.

Public Health Action
State and local health departments and agencies can continue to use BRFSS data to identify populations at risk for certain unhealthy behaviors and chronic diseases and conditions. Data also can be used to design, monitor, and evaluate public health programs at state and local levels.


Surveillance for Certain Health Behaviors and Conditions Among States and Selected Local Areas ? Behavioral Risk Factor Surveillance System, United States, 2013 and 2014


Sonya Gamble, MS,1 Tebitha Mawokomatanda, MSPH,corresponding author1 Fang Xu, PhD,1 Pranesh P. Chowdhury, MD,1 Carol Pierannunzi, PhD,1 David Flegel, MS,2 William Garvin,1 and Machell Town, PhD1

Publish date

2017 Sep 15;